ERICA: Enabling real-time mistake detection and corrective feedback for free-weights exercises

• Main Authors: RADHAKRISHNAN, Meeralakshmi, RATHNAYAKE, Darshana, ONG, Koon Han, HWANG, Inseok, MISRA, Archan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: smart gym; digital personal trainer; earables; free-weights exercises; Internet of things (IoT); personalized feedback; Software Engineering; Sports Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/5894; https://ink.library.smu.edu.sg/context/sis_research/article/6897/viewcontent/29._ERICA_Enabling_Real_time_Mistake_Detection___Corrective_Feedback_for_Free_Weights_Exercises__Sensys2020_.pdf
• Institution: Singapore Management University

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.